Large-scale multi-omics enhance risk prediction for type 2 diabetes
Polygenic risk scores (PRS), metabolomics, and proteomics have each shown promise in improving type 2 diabetes risk prediction, but their combined utility beyond established clinical models remains unclear. We aimed to evaluate whether integrating multi-omics biomarkers enhances 10-year type 2 diabe...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
28 May 2026
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| In: |
Cardiovascular diabetology
Year: 2026, Volume: 25, Issue: 1, Pages: 1-13 |
| ISSN: | 1475-2840 |
| DOI: | 10.1186/s12933-026-03223-y |
| Online Access: | Verlag, kostenfrei, Volltext: https://link.springer.com/article/10.1186/s12933-026-03223-y Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s12933-026-03223-y 
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| Author Notes: | Ruijie Xie, Christian Herder and Ben Schöttker |
| Summary: | Polygenic risk scores (PRS), metabolomics, and proteomics have each shown promise in improving type 2 diabetes risk prediction, but their combined utility beyond established clinical models remains unclear. We aimed to evaluate whether integrating multi-omics biomarkers enhances 10-year type 2 diabetes risk prediction beyond single-omics extensions and the clinical Cambridge Diabetes Risk Score (CDRS), which includes HbA1c measurements. |
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| Item Description: | Gesehen am 10.09.2026 |
| Physical Description: | Online Resource |
| ISSN: | 1475-2840 |
| DOI: | 10.1186/s12933-026-03223-y |